Helpful examples of using Stacking machine learning algorithms in scikit-learn.
The Stacking (Stacked Generalization) algorithm is an ensemble learning technique that combines multiple base models to improve predictive performance.
Unlike bagging and boosting, which build models of the same type, stacking leverages the strengths of different types of models by training them in parallel on the same dataset.
The predictions of these base models are then used as input features to train a meta-model, also known as a blender or level-1 model. This meta-model learns to make the final prediction by identifying and combining the strengths of the base models. The process involves two stages: first, training the base models, and second, using their predictions to train the meta-model.
Stacking is powerful because it can harness the diverse capabilities of various algorithms, leading to superior performance and robustness. However, it requires careful tuning and validation to avoid overfitting and to ensure the meta-model effectively integrates the base models’ predictions.